Applications of Computational Intelligence

Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-...

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Bibliographic Details
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
Subjects:
CSI
Online Access:Open Access: DOAB: description of the publication
Open Access: DOAB, download the publication
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720 1 |a Gong, Maoguo  |4 edt 
720 1 |a Gong, Maoguo  |4 oth 
720 1 |a Miao, Qiguang  |4 edt 
720 1 |a Miao, Qiguang  |4 oth 
720 1 |a Qin, Kai  |4 edt 
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720 1 |a Wu, Yue  |4 oth 
245 0 0 |a Applications of Computational Intelligence 
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520 |a Computational Intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems. Over the last few years, there has been an explosion of research on deep learning, specifically deep convolutional neural networks, and deep learning has become the core method for artificial intelligence. In fact, some of the most successful AI systems today are based on CI. Therefore, this reprint focuses on the theoretical study of computational intelligence and its applications. 
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650 7 |a Computer science  |2 bicssc 
650 7 |a Information technology industries  |2 bicssc 
653 |a active-frozen memory model 
653 |a AlphaZero 
653 |a artificial intelligence 
653 |a attention mechanism 
653 |a capsule network (CapsNet) 
653 |a circle chaotic map 
653 |a computational intelligence 
653 |a convolutional neural network 
653 |a convolutional neural network (CNN) 
653 |a convolutional neural networks 
653 |a crop disease leaf image segmentation (CDLIS) 
653 |a crop insect pest identification 
653 |a cross-modal learning network 
653 |a crystal structure algorithm 
653 |a CSI 
653 |a data-driven method 
653 |a deep learning 
653 |a dilated convolution 
653 |a disease screening 
653 |a electroencephalogram 
653 |a engineering optimization problems 
653 |a evolutionary algorithm 
653 |a evolutionary multitasking 
653 |a evolutionary optimization 
653 |a few-shot learning 
653 |a gastrointestinal stromal tumor 
653 |a golden sine algorithm 
653 |a graph neural network 
653 |a hashing learning 
653 |a HCNNs 
653 |a human perception 
653 |a hyperspectral image 
653 |a image classification 
653 |a image retrieval 
653 |a joint integrated probabilistic data association 
653 |a knowledge distillation 
653 |a Kuroshio Extension Observatory 
653 |a large-scale multiobjective optimization 
653 |a levy flight 
653 |a Levy flight 
653 |a lightweight multi-scale dilated U-Net (LWMSDU-Net) 
653 |a medical image classification 
653 |a medical image segmentation 
653 |a multi-scale convolution-capsule network (MSCCN) 
653 |a multi-target tracking 
653 |a multipopulation optimization 
653 |a neuroevolution 
653 |a no-limit Texas hold'em 
653 |a NoGo games 
653 |a nonlinear adaptive weight 
653 |a object detection 
653 |a online update 
653 |a opponent exploitation 
653 |a particle swarm optimization 
653 |a people counting 
653 |a progressive deep learning 
653 |a propagation mechanism 
653 |a quality of experience 
653 |a random finite set 
653 |a reinforcement learning 
653 |a reliable evaluation strategy 
653 |a self-attention 
653 |a self-organizing map 
653 |a self-training 
653 |a semi-supervised learning 
653 |a sound speed profile 
653 |a sparse unmixing 
653 |a spatial attention 
653 |a transfer learning 
653 |a Transformer 
653 |a tuna swarm optimization 
653 |a U-Net 
653 |a visual tracking 
653 |a weakly supervised segmentation 
793 0 |a DOAB Library. 
856 4 0 |u https://directory.doabooks.org/handle/20.500.12854/100013  |7 0  |z Open Access: DOAB: description of the publication 
856 4 0 |u https://mdpi.com/books/pdfview/book/7106  |7 0  |z Open Access: DOAB, download the publication